Integrating Google Analytics 360 and BigQuery improves efficiency and insights for Skyscanner

Skyscanner is a leading global travel search company covering flights,hotels and car hire around the world. Founded in 2003, the company helps over 40 million people each month find the best travel options across its portfolio of websites and mobile apps.

Skyscanner wanted to understand the anonymised behaviour of consumers on a more granular level than was possible via standard reports in the Google Analytics 360 web interface, or even using the reporting APIs. “These methods work well for high-level analysis and standard marketing reporting, but we were keen to dig deeper into the data to get more insight and further optimise our products,” explains Mark Shilton, Principal Analyst in the Skyscanner Data Team.

For example, the company wanted to create detailed cohorts to understand how users interacted with Skyscanner over time. Also, different teams in the business were keen to understand the performance of individual pieces of functionality that fell within their remit. To do this, they needed to understand not just the overall conversion rate, but how users who interact with a given piece of functionality convert compared to those who do not. Skyscanner also wanted drill down into specific markets, devices types and marketing channels.

Mapping a plan for deeper insights

Skyscanner opted to address all of these needs by integrating Google Analytics 360 with BigQuery. The integration has become the starting point for many detailed investigations across the business. For example, analysts and engineers now run cohort analyses to understand how frequently users return to Skyscanner, and which channels are most effective at which part of the customer journey. “This type of analysis is allowing a much deeper understanding of our marketing activity and is informing our future strategy and spend,” Mark says.

He reveals that using BigQuery in conjunction with other tools such as Tableau and Python also helps Skyscanner execute analysis much more quickly and efficiently than before. “While in the past it was tricky to get a fully unsampled report based on specific segments of users flowing directly from Google Analytics 360 into a Tableau dashboard, now it is simply a matter of writing the query, creating a connection in Tableau to automatically refresh the data daily, and publishing this dashboard to the rest of the company.”

Another key benefit of using a flexible combination of tools is the ability to keep an eye on costs. “Where the aggregations and segments of data are required on a regular basis, we have to consider the potential cost of querying the entire BigQuery dataset multiple times for the same data,” Mark explains. “To minimise this, we use Python scripts to automate these aggregations into new, smaller tables that are much more cost efficient to query.”

Excellent visibility and a clear path ahead

Combining Google Analytics 360 with BigQuery has supercharged Skyscanner’s ability to turn raw data into deep understanding of consumer behaviour. “We have been using BigQuery for various pieces of analysis, from one-off investigations to powering daily dashboards,” Mark affirms. “In all cases it has speeded up our workflow and enabled us to gain greater insight more quickly. In a fast moving internet economy, this is key. Instead of setting up and scheduling one or more unsampled API reports, analysts can now write a query against BigQuery and have results almost instantly.”

“BigQuery has also allowed us to more easily isolate the effects of marketing from the effects of site changes,” Mark says. “By writing queries that focus on the conversion rate from specific pages in the funnel, we can better segment our traffic. We can separate traffic from various sources, including: specific marketing campaigns, users who make it to specific parts of the funnel, or users who interact with new functionality. This greater understanding has played a key role in improving overall conversion rates on our websites, particularly on mobile where we’ve achieved conversion rate improvements of 30 to 40% on smartphone and tablet devices in the last six months.”

Looking to the future, Skyscanner’s next steps include exploring how this data can be used to segment, cluster and classify users for machine learning analyses, which would not have been possible using standard reporting functionality.